Medium Term Electric Load Forecasting Using TLFN Neural Networks
Keywords:
Neural network model, forecasting, gamma memory, electric loadAbstract
This paper develops medium term electric load forecasting using neural networks, based on historical series of electric load, economic and demographic variables. The neural network chosen for this work is the Time Lagged Feedforward Network (TLFN), which combines conventional network topology (multilayer perceptron) with good handling of time dependencies by means of gamma memory. This is a versatile mechanism that generalizes the short term structures of memory, based on delays and recurrences. This scheme allows smaller adjustments without requiring changes in the general network structure. The neural model gave satisfactory results exceeding those obtained by classical statistical models like multiple linear regression.References
Bengio, S., Fesant, F., Collobert D., A Connectionist System for Medium - Term Horizon Time Series Prediction, International Workshop on Applications of Neural Networks to Telecommunications, IWANNT, Stockholm, Sweden, 1995.
Bishop, C., Neural Networks for Pattern Recognition, Oxford University Press, 1995.
Costa, N., Ribeiro, B., A neural prediction model for monitoring and fault diagnosis of a plastic injection moulding process, Centro de Informática e Sistemas da Universidade de Coimbra, 1999.
Freeman, J. and Skapura D., Redes Neuronales: Algoritmos, Aplicaciones y Técnicas de Programación, Versión en espa-ol de GarcÃa-Bermejo, R. Joyanes, L. Addison Wesley / DÃaz de Santos, Wilmington, Delaware, EUA, 1993.
GarcÃa, J. and GarcÃa, F., EconometrÃa. Tema 5. Autocorrelación, Curso 2002/2003. Universidad de Huelva, 2002.
Gavrilas, M., Neural Network based Forecasting for Electricity Markets, Technical University of Iasi, Romania, 2002.
Hagan, M. and Behr, S., "The Time Series approach to short term load forecasting", IEEE Transactions on Power Systems, Vol. PWRS-2, NO. 3, 1997.
Instituto Nacional de EstadÃsticas (INE 2002b), Censo 2002. SÃntesis de Resultados.
Karady, G., Short - Term Load Forecasting using Neural Networks and Fuzzy Logic, Arizona State University. Power Zone, 2001.
Khotanzad, A., Abaye A., "ANNSTLF - A Neural Network Based Electric Load Forecasting System". IEEE Trans. on Neural Networks, Vol. 8, No. 4, 1997. http://dx.doi.org/10.1109/72.595881
MartÃn del BrÃo, B., Sanz Molina A., Redes Neuronales y Sistemas Borrosos, 2da. Edición, Ra-Ma, Madrid, Espa-a, 2001.
Moro, Q., Series Temporales y Redes Neuronales Artificiales, Departamento de Informática Universidad de Valladolid. Pérez, C. (2001), Técnicas EstadÃsticas con SPSS, Prentice Hall, Madrid, Espa-a, 2002.
Principe J., Euliano N., Lefebvre W., Neural and Adaptive Systems: Fundamentals Through Simulations, John Wiley and Sons, New York, 2000.
Refenes, A., Burgess, A., Bentz, Y., "Neural Networks in Financial Engineering: A Study in Methodology". IEEE Transactions in Neural Networks, Vol 8; No. 6, 1997. http://dx.doi.org/10.1109/72.641449
Spiegel, M., EstadÃstica, Serie de Compendios Schaum. Libros McGraw-Hill, Colombia, 1969.
Torche, A., Contabilidad Nacional. NúmerosIndices. Desestacionalización y Trimestralización, Trabajo EstadÃstico Número 63. Pontificia Universidad Católica de Chile. Insituto de EconomÃa. Santiago, 1998.
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